an analysis of impact of policy on carbon...

AN ANALYSIS OF THE IMPACT OF POLICY AND OTHER FACTORS ON CARBON MONOXIDE EMISSIONS

Growing environmental concern throughout the world has ignited a number of controversial views on how environmental cleaning should be achieved. In the context of developing countries in particular, several options have been put forth. Environmentalists have held that general level production and consumption are the prime drivers of environmental pollution and only in reducing economic activity can environmental performance be improved. Other opinions have favoured a reduction in population among developing countries as a key step to resolving the environmental dump, especially in their larger cities. Yet another perspective has emphasised that more than population and consumption, the answer lies in formulating and implementing appropriate policies.

To understand the relative significance of each of these opinions, it would be most useful to directly look at a cross-section of developing countries to see how their environmental performance has been linked to the above factors. However, a paucity of consistent data makes this exercise difficult. A possible alternative could be to conduct the relevant data analysis using information on the US, and to extrapolate, to the extent possible, the broad implications of the results for developing countries. Data relating to environmental emissions should ideally comprise information on Nitrogen Oxides, Carbon Monoxide, Volatile Organic Compounds and Sulfur Dioxide. Using these, a global air quality variable should be obtained. However, in light of the mathematical complications is would produce, it is simpler to look at one emission variable.

Response Variable: I will choose Carbon Monoxide (CO) per capita as the response variable, since this gas has been intimately connected to car related pollution, a source of growing concern among developing countries as their number of cars increase and pollute the environment. This data on CO emissions is available at www.epa.gov. I have divided this data by the population over time to reduce the time effect of emissions to the extent that population tends to go up over time. I will be using data from 1960 to 1989, as two policy variables we will be examining occurred during this period. The units of measurement are in millions of metric tonnes.

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The plot of CO per capita versus time shows that for until about 1970, the emission levels per capita were quite level, they began to reduce consistently after that. This downward trend post about 1970 has continued through until 1989. The reason for the downturn in emission per capita could be related to the Clean Air Act of 1970. Interestingly, although the Clean Air Act of 1963 was in place earlier, according to the above graph, this policy did not seem to have the same dramatic effect on CO per capita levels. Among the predictor variables, I chose GNP per capita. I would expect that this general indicator of the level of economic activity in the country would have some correlation with the level of CO per capita emissions. A look at the scatter plot of CO per capita and GNP per capita shows a similar pattern as the time series plot of the response variable, which is that there is a flat relationship initially, which then becomes quite obviously negative after about 1972.

I also chose gasoline consumption per capita over time, since the consumption gasoline over time (GCPC) is an important indicator of vehicle related pollution. To the extent that some gasoline may be used for purposes other than for consumption in vehicles, my result may be biased. However, from what I know, gasoline is used almost entirely for vehicles, so there should not be any significant issue with respect to this variable.

Once again, we see a similar pattern in the plot between COPC and gasoline consumption per capita, namely that the relationship remains quite level up to a point, after which it becomes quite negative. The effect here is a little delayed, since the downturn begins to occur after 1973. This could perhaps be the result of the policy put into place in 1970, and after a lag of a few years, adjustments in the industry may have resulted in higher gasoline consumption per capita being associated with lower levels of CO per capita. Another predictor variable I chose was the extent of forest fires in the country. This variable is important because forest fires are known to release substantial amounts of CO in the air. The data for this predictor variable was obtained from www.usda.gov. and is measured in million acres of land destroyed by forest fires.

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http://www.usda.gov/

Here we do not see a distinct pattern emerge and it is difficult to guess whether our model will require this variable. However, it may be good to use it initially and see the results it gives.

Along with the above variables, I also chose to look at the CAA 1970 and CAA 1963 to see whether these policies had any dramatic impact on the CO per capita emissions.

I would expect both these variables to have an impact on the level of CO per capita emissions, although looking at the scatter plots, I would expect CAA of 1970 to be of greater value in our model.

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In addition to the above variables, I will include time as a variable. As the scatter plots above show, time seems to play an important role in the manner in which the individual relationships of the predictor variables seem to be related to the response variable.

In running the regressions with CO per capita as my response variable and GNP per capita, gasoline consumption per capita, area under forest fire and the policies of 1963 and 1970 as my predictor variables, I get the following results:

The errors appear normally distributed. However, the plot of standardized residuals over time indicates that autocorrelation might possibly be a problem. However, I will not correct for it here, but will be aware that the results may have an element of bias in them.

Let us then look at our regression results (MINITAB output).

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The regression equation is

COPC = 24443 + 0.0197 GNP P.C + 40.7 Pol63 + 50.9 Pol70 + 3.86 For.fires + 78.4 GCPC - 12.3 Year

Predictor Coef StDev T P VIFConstant 24443 9185 2.66 0.014GNP P.C 0.01970 0.03133 0.63 0.535 288.9Pol63 40.72 11.44 3.56 0.002 2.6Pol70 50.90 12.69 4.01 0.001 4.2For.fire 3.859 2.737 1.41 0.171 1.4GCPC 78.44 81.98 0.96 0.348 15.7Year -12.256 4.758 -2.58 0.017 206.7

S = 16.48 R-Sq = 95.3% R-Sq(adj) = 94.2%

Analysis of Variance

Source DF SS MS F PRegression 6 132965 22161 81.57 0.000Residual Error 24 6521 272Total 30 139486

Source DF Seq SSGNP P.C 1 108698Pol63 1 842Pol70 1 15545For.fire 1 5GCPC 1 6073Year 1 1802

Unusual ObservationsObs GNP P.C COPC Fit StDev Fit Residual St Resid 4 6378 608.57 637.23 10.16 -28.66 -2.21R 31 11272 386.29 382.17 14.21 4.12 0.49 X

R denotes an observation with a large standardized residualX denotes an observation whose X value gives it large influence.

Durbin-Watson statistic = 1.16

The first thing that strikes me is the high VIF statistics, indicating a major multicollinearity problem. It is quite likely that GNP per capita, gasoline consumption per capita and time have a strong correlation. This could explain some of the low t-statistics for the coefficient estimates, while the F statistic is a high 81.57, indicating that we can strongly reject the hypothesis that this model has no explanatory power. The R square too is over 94, indicating that about 94 % of the variability in the response variable can be explained by our model.

Another thing which stands out is the fact that both the policy variables are positively correlated with CO per capita emissions. This is completely counterintuitive and can perhaps be explained by the fact that after about 1970, when the second CAA came into effect, the relationships between several of the explanatory variables and CO per capita changed. Since the slope of the relationship changed after this period, it may be more accurate to split the data explicitly into two and run separate regressions for the period before 1970 and after.

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For the period before the second CAA act (i.e. between 1960 and 1970), let us look to see whether our assumptions are violated. The errors do not seem very normally distributed, but are not very non-normal either. Some outliers do seem to be present and we will come back to this. The residual shows some cyclical pattern, although this does not seem to be a major problem. No major increases or decreases in variance are visible.

Let us then see how our regression results look.

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The regression equation isCOPC = 14592 + 0.0077 GNP P.C - 0.024 For.fires + 259 GCPC - 7.25 Year + 11.5 Pol63

Predictor Coef StDev T P VIFConstant 14592 3937 3.71 0.014GNP P.C 0.00772 0.01115 0.69 0.520 168.0For.fire -0.0235 0.8061 -0.03 0.978 2.9GCPC 258.98 65.04 3.98 0.011 105.4Year -7.254 2.034 -3.57 0.016 108.6Pol63 11.515 3.420 3.37 0.020 7.1

S = 2.047 R-Sq = 98.2% R-Sq(adj) = 96.4%


Source DF SS MS F PRegression 5 1144.08 228.82 54.60 0.000Residual Error 5 20.95 4.19Total 10 1165.04

Source DF Seq SSGNP P.C 1 1001.07For.fire 1 45.22GCPC 1 19.03Year 1 31.27Pol63 1 47.50

Unusual ObservationsObs GNP P.C COPC Fit StDev Fit Residual St Resid 11 8134 627.796 625.946 1.842 1.850 2.07R

R denotes an observation with a large standardized residual


The regression seems to show a reasonably good fit. The R square indicates that about 96 % of the variability in CO per capita emission can be explained by our model. We should be aware that some of the fit may possibly be a result of an autocorrelation problem which may be inflating the fit somewhat. The F-statistic indicates that we can strongly reject the null that the model has no explanatory power. Besides GNP per capita and forest fires show a high enough p-value to not reject the null that these variables have no explanatory power on their own.

In carrying out the regression diagnostics, no major outliers or leverage points were apparent.

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Also, multicollinearity seems to be a problem with the model. Some of this must

continue to be due to the strong relationship between GNP per capita and gasoline consumption per capita.

Clearly, the above plots indicate a very strong relationship between GNP per capita and time, gasoline consumption per capita and time and GNP per capita and gasoline consumption per capita. It would seem that the overall consumption level in the economy, as embodied by the GNP per capita data may be less useful than the gasoline consumption data, which is likely to relate more directly to CO emission. Therefore, GNP per capita can be dropped. Since time trend seems to be an important issue in this model,

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let us keep it. We therefore run a regression with CO per capita as the response and area under forest fire, CAA, 1963, per capita gasoline consumption and time as predictor variables. Before discussing the results, let us see whether our errors are more or less in keeping with our assumptions.

The errors look reasonably well distributed. There does not seem to be a problem of heteroskedasticity. The plot of standard residuals versus time does not indicate a serious autocorrelation problem either.

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Let us now analyse the results and see whether this is an improvement on the previous one.Regression Analysis

The regression equation isCOPC = 13020 + 12.8 Pol63 + 288 GCPC - 6.44 Year - 0.392 For.fires

Predictor Coef StDev T P VIFConstant 13020 3073 4.24 0.005Pol63 12.790 2.753 4.65 0.004 5.0GCPC 287.64 47.92 6.00 0.001 62.7Year -6.438 1.584 -4.06 0.007 72.1For.fire -0.3916 0.5787 -0.68 0.524 1.7

S = 1.956 R-Sq = 98.0% R-Sq(adj) = 96.7%



Source DF Seq SSPol63 1 192.48GCPC 1 876.22Year 1 71.62For.fire 1 1.75


As the results indicate, the multicollinearity problem is reduced now, but definitely not eliminated. Clearly, the relationship between time and gasoline consumption per capita has to do with this. However, since we are not breaking any assumptions of the model, we will stick with both the variables since they both seem to provide us with valuable information.

The model contained two values which were influential points: one was the year 1960 and the other, the year 1970. I ran a regression without these points and did not see a dramatic change in my results. Also, because I have so few points in my data set, I do not wish to lose further information and will keep the data from these two years intact.

The adjusted R square of close to 97 % indicates that a very high proportion of the variability in the CO per capita emission can be explained by our model. The F-statistic indicates that we can strongly reject the null hypothesis that the model has no predictive power. In addition, the low p-values of all the explanatory variables, except the forest fire variable indicate that individually, they do contribute to explaining variability in CO per capita emissions. As we had seen in the scatter plot of CO per capita and forest fire,

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no apparent relationship seemed to exist between the two variables. This is now borne out by the high p value of 0.524. It may be worthwhile to remove this variable as it seems to be adding noise to our model. Hence we run a regression with CO per capita as response variable and gasoline consumption per capita, time and CAA, 1963 as predictors.

Before we look at the results, let us see whether our assumptions are violated.

The graphs look fairly reasonable, except that once again, an autocorrelation problem may perhaps exist in our model. This means that our estimates may have some element of bias in them.

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Looking at the regression results, we note that removing the forest fire variable has improved our model in the sense that all our predictor variables now have p values which show that independently, the variables have explanatory power. The adjusted R square is still very high. It indicates that about 97 % of the variability in the data may be explained by the model. However, some of this high R square may perhaps be due to a slight autocorrelation problem in our model. The F statistic of 107 indicates clearly that the model does have explanatory power.

Regression Analysis

The regression equation isCOPC = 11956 + 271 GCPC + 11.6 Pol63 - 5.89 Year

Predictor Coef StDev T P VIFConstant 11956 2536 4.71 0.002GCPC 271.25 39.72 6.83 0.000 46.7Pol63 11.629 2.069 5.62 0.001 3.1Year -5.890 1.308 -4.50 0.003 53.3

S = 1.879 R-Sq = 97.9% R-Sq(adj) = 97.0%



Source DF Seq SSGCPC 1 1028.79Pol63 1 39.91Year 1 71.62


There continue to be two influential points in the data: these are the end points: 1960 and 1970. When I removed these in my present model, it gave me a better fit, although, as stated earlier, the compromise was that I lost valuable information because I have so few data points.

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Regression Analysis

The regression equation isCOPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year

Predictor Coef StDev T P VIFConstant 7893 2409 3.28 0.022GCPC 200.92 35.00 5.74 0.002 64.5Pol63 12.198 1.826 6.68 0.001 4.7Year -3.794 1.241 -3.06 0.028 84.3

S = 1.047 R-Sq = 99.4% R-Sq(adj) = 99.0%



Source DF Seq SSGCPC 1 828.30Pol63 1 56.70Year 1 10.24


Now, as we can see the adjusted R square is an even higher 99 %.

The model:

COPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year

states that as gasoline consumption per capita increases by 1 unit, CO per capita emission increases by 201 million tonnes provided other variables are held constant. The time variable coefficient indicates that with the passage of one year, CO per capita reduced by 3.79 million tonnes. Interestingly, the CAA 1963 variable is positively correlated with CO per capita. This indicates that the first CAA act did not really help curb the CO emission problem, which explains why the second one was introduced some years later!

Turning now to the second part of the data (1970-1990), let us first look at the residual plots which come from running a regression of GNP per capita, forest fires, gasoline consumption per capita and time. I will not include the policy variable of 1970, since it was in place throughout the period we will be looking at.

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We first look at the error structure to determine whether or not any of our assumptions

are violated.

The errors seem reasonably normally distributed. Some autocorrelation may possibly be present in our data, and we have to be aware of this factor while evaluating our results. The errors seem to have a dispersion which indicates that heterskedasticity is not a problem in this model.

The regression result is presented below.

The regression equation isCOPC = 20472 + 0.0044 GNP P.C + 5.10 For.fires + 1.1 GCPC - 10.1 Year

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Predictor Coef StDev T P VIFConstant 20472 9375 2.18 0.047GNP P.C 0.00440 0.03159 0.14 0.891 65.3For.fire 5.104 3.120 1.64 0.124 1.3GCPC 1.14 94.90 0.01 0.991 2.7Year -10.110 4.851 -2.08 0.056 62.7

S = 14.62 R-Sq = 93.9% R-Sq(adj) = 92.2%



Source DF Seq SSGNP P.C 1 43802For.fire 1 141GCPC 1 1334Year 1 929

Unusual ObservationsObs GNP P.C COPC Fit StDev Fit Residual St Resid 2 8562 615.16 587.68 6.19 27.47 2.07R 18 11186 478.24 453.38 7.97 24.86 2.03R



As in the previous model, there is a problem of multicollinearity again. This can explain why none of the coefficient estimates appear to be statistically significant, even though the F statistic is over 54. This time, it seems largely between GNP per capita and time. To check for this, let us look at the scatter plots. Unlike the previous case, the scatter plot between gasoline per capita and time and GNP per capita and gasoline consumption per capita do not show a strong correlation. Hence, to reduce our multicollinearity problem, it may be a good idea to drop the GNP per capita variable. Once again, the gasoline per capita consumption will continue to capture the car-related pollution.

Looking at the regression model after dropping GNP per capita, we first note that the errors are reasonably well distributed and there does not seem to be serious heteroskedasticity problem. However, once again, some autocorrelation may possibly be

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present in the data..

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Now we can look at the regression result.

Regression Analysis

The regression equation isCOPC = 19178 + 5.06 For.fires + 11.5 GCPC - 9.44 Year

Predictor Coef StDev T P VIFConstant 19178 1301 14.75 0.000For.fire 5.061 3.002 1.69 0.113 1.3GCPC 11.54 56.71 0.20 0.841 1.0Year -9.4400 0.6631 -14.24 0.000 1.3

S = 14.14 R-Sq = 93.9% R-Sq(adj) = 92.7%



Source DF Seq SSFor.fire 1 4986GCPC 1 716Year 1 40500

Unusual ObservationsObs For.fire COPC Fit StDev Fit Residual St Resid 2 2.60 615.16 587.84 5.89 27.32 2.13R

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18 5.70 478.24 453.21 7.61 25.03 2.10R



Now the multicollinearity problem seems to taken care of. Interestingly, the forest fire and the gasoline consumption coefficients continue to not be significantly different from zero.

The adjusted R square indicates that about 92% of the variability in CO emission per capita can be explained by the model and the F statistic indicates clearly that we can reject the null hypothesis that the model has no explanatory power.

We note that two points at the top and two at the bottom of the normal plot stand out as a little different. Conducting a regression diagnostic, I find that these points have the highest standard residuals in this data. However, since these residuals range from about –1.46 to 2.12, these are not potent enough to remove. Their Hi values and Cook’s distance values too are within the norm.

The next thing to do would be to remove the variables for forest fire and gasoline consumption and see how this affects the model.

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Checking for assumptions, we note the same issues as earlier, which is that the errors are normally distributed and do not seem to demonstrate heteroskedasticity. However,

autocorrelation may be present here too. Four points stand out the above graphs as unusual. However, conducting a regression diagnostic, none of them turn out to be major outliers, leverage or influential points.

We now look at the regression model.

Regression Analysis

The regression equation isCOPC = 18234 - 8.95 Year

Predictor Coef StDev T PConstant 18234 1201 15.18 0.000Year -8.9475 0.6067 -14.75 0.000

S = 14.48 R-Sq = 92.8% R-Sq(adj) = 92.3%



Unusual ObservationsObs Year COPC Fit StDev Fit Residual St Resid 18 1988 478.24 446.72 5.88 31.52 2.38R


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As we can see, the univariate model appears to explain about 92 % of the variation in CO per capita emission. The F statistic is high and shows that we can strongly reject the null hypothesis that the model has no explanatory power.

We can interpret the results of this model to mean that after 1970, an increase of a year was associated on an average with a drop of 9 million tonnes in CO per capita.

CONCLUSION

Splitting the data into a period pre CAA 1970 and post 1970 shows that two different models were useful for understanding how CO per capita emission evolved over time.The models are:COPC = 7893 + 201 GCPC + 12.2 Pol63 - 3.79 Year For the initial period, and for the second period:

COPC = 18234 - 8.95 Year

Clearly, in the first period, level of gasoline consumption per capita did have a significant impact on the level of CO emission per capita, as did the time. In the second period, however, only the time trend seems to be relevant. This could be indicative of the fact that the policy of 1979 had a major impact on the CO per capita emission, which is captured in the time variable.

From these findings we can then conclude that gasoline consumption ( and therefore vehicle usage) is significantly linked with higher levels of CO per capita emission, if the policy in place is not a very effective one. Hence, in the developing country context, this means that restricting consumption of output is not necessarily the path to environmental salvation.

On the other hand, if a strong and effective policy is put into place, while giving industry and individuals sufficient time to adapt to the regulation, this is likely to be strongly linked with a sustained reduction in CO per capita emissions. Another significant factor to be kept in mind is that the level of technology in a developing country must be adequately advanced to incorporate the changes desired by the policy makers. It should however be kept in mind that the decline in emission may be gradual, as the old cars on the road are replaced by new ones, for instance. This is perhaps what also explains the declining trend over time in the US.

If these factors are kept in view, then the chances are high they will show results similar to the ones we have obtained for the US.

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an analysis of impact of policy on carbon...

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